Abstract : As an appealing topic in pattern recognition, handwritten mathematical expression recognition
exhibits a big research challenge and underpins many practical applications. Both a large set of symbols
(more than 100) and 2-D structures increase the difficulty of this recognition problem. In this thesis, we
focus on online handwritten mathematical expression recognition using BLSTM and CTC topology, and
finally build a graph-driven recognition system, bypassing the high time complexity and manual work
in the classical grammar-driven systems. To allow the 2-D structured language to be handled by the
sequence classifier, we extend the chain-structured BLSTM to an original Tree-based BLSTM, which could
label a tree structured data. The CTC layer is adapted with local constraints, to align the outputs and at the
same time benefit from introducing the additional ’blank’ class. The proposed system addresses the
recognition task as a graph building problem. The input expression is a sequence of strokes, and then an
intermediate graph is derived considering temporal and spatial relations among strokes. Next, several
trees are derived from the graph and labeled with Tree-based BLSTM. The last step is to merge these
labeled trees to build an admissible stroke label graph (SLG) modeling 2-D formulas uniquely. One major
difference with the traditional approaches is that there is no explicit segmentation, recognition and layout
extraction steps but a unique trainable system that produces directly a SLG describing a mathematical
expression. The proposed system, without any grammar, achieves competitive results in online math
expression recognition domain.